We implement our network based on pytorch, using four 2080Ti GPUs. Pose Encoder contains 4 down-sampling layers, decomposed attribute encoder contains 3 down-sampling layers, and texture renderer is composed of 3 SPADE ResBlks. Discriminator is composed of three down-sampling layers. In each layer of the network, spectrum normalization [1]} is used to stabilize network training. We use the Adams optimizer [2]} with \(\beta _1=0.5\)  and \(\beta _2=0.999\) . Inspired by TTUR [3]}, we set the initial learning rates of the generator and discriminator to 0.0002 and 0.0003, respectively. The batchsize is set to 4, the training process lasts for 30 epochs, and the learning rate is linearly decayed to 0 after 15 epochs.
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